Stochastic complexity in identification of continuous time systems

The purpose of this paper is to present a continuous time identification method which can be used for high accuracy prediction and control. We consider continuous time systems with quasi-periodic inputs and white observation noise. These investigations have been motivated by control problems in microrobotics, where sampling rate and accuracy requirements are very high. It is shown that continuous time identification methods lead to numerically well conditioned prediction. The key tool in showing this is a general result of the theory of stochastic complexity. Also, we give an explanation on why discrete time methods break down.